FC
EA
15 August 2016
Preparing the data
### Notes
# install.packages('ggplot2',dependencies = T)
# Test code line with command+enter
# update.packages(checkBuilt = T,ask = F)
# head(ds) # first few lines of a dataframe
# str(ds) #show structure of dataframe
###
# Setting variables:
options(width=108)
outT <- 2.5 # Outlier factor
# Loading libraries
library(ggplot2) # for plotting
library(plyr)
#Importing the data
#data_dir <- '/Volumes/PK/'
homeDir <- path.expand('~')
dataDir <- paste(homeDir, 'Dropbox/Projects/fc/fc/data/usable/extracted', sep='/')
dataFiles <- dir(dataDir, pattern='csv')
nSubj <- length(dataFiles) # number of subjects
# Going through all subjects' data:
df <- data.frame() # data frame that combines all subjects' data into a single ds
curSubjN <- 6 #temp
for(curSubjN in 1:nSubj){
# Loading the data;
ds <- read.csv(paste(dataDir, dataFiles[curSubjN], sep='/'))
# Shorter names for variables:
colnames(ds) <- c('subjId', 'domEyeR', 'threshStHi', 'threshStLo', 'thresh', 'trialN', 'sentId',
'sentPx', 'congr', 'fam', 'locTop', 'cued', 'corr', 'broken', 'st')
# Non-blank data set:
cds <- ds[ds$sentId<31,]
# Removing the outliers:
outlLo <- cds$st>(mean(cds$st)-outT*sd(cds$st)) # binary for every row
outlHi <- cds$st<(mean(cds$st)+outT*sd(cds$st))
cds$outlNlo <- sum(!outlLo) # counting the number of outliers before removing
cds$outlNhi <- sum(!outlHi)
cds <- cds[outlLo,] # removing the outliers
cds <- cds[outlHi,]
cds <- cds[!is.na(cds$subjId),] # usually don't need this; remove NA rows
# Normalized suppression times:
cds$stNorm <- cds$st / mean(cds$st)
# Binding the subject ds to the common data frame:
df <- rbind(df, cds)
}
# Prettying up the data frame:
df$subjId <- factor(df$subjId)
df$Familiarity[df$fam==1] <- 'Familiar\n(Upright)'
df$Familiarity[df$fam==0] <- 'Unfamiliar\n(Inverted)'
df$Congruency[df$congr==1] <- 'Congruent'
df$Congruency[df$congr==0] <- 'Incongruent'
df$Attention[df$cued==1] <- 'Cued'
df$Attention[df$cued==0] <- 'Uncued'
# Binning
df$bin <- 6
df$bin[df$trialN<500] <- 5
df$bin[df$trialN<400] <- 4
df$bin[df$trialN<300] <- 3
df$bin[df$trialN<200] <- 2
df$bin[df$trialN<100] <- 1
head(df)## subjId domEyeR threshStHi threshStLo thresh trialN sentId sentPx congr fam locTop cued corr broken
## 1 1 1 0.24862 0.2398 0.24421 1 10 124 1 0 0 0 1 1
## 2 1 1 0.24862 0.2398 0.24421 2 15 121 0 1 1 1 1 1
## 3 1 1 0.24862 0.2398 0.24421 3 28 100 0 0 0 0 1 1
## 4 1 1 0.24862 0.2398 0.24421 4 28 100 0 0 1 1 1 1
## 5 1 1 0.24862 0.2398 0.24421 5 2 133 0 1 0 0 1 1
## 6 1 1 0.24862 0.2398 0.24421 6 12 117 0 0 1 1 1 1
## st outlNlo outlNhi stNorm Familiarity Congruency Attention bin
## 1 1.1426 1 7 0.8461978 Unfamiliar\n(Inverted) Congruent Uncued 1
## 2 1.7793 1 7 1.3177312 Familiar\n(Upright) Incongruent Cued 1
## 3 1.3451 1 7 0.9961672 Unfamiliar\n(Inverted) Incongruent Uncued 1
## 4 1.4134 1 7 1.0467494 Unfamiliar\n(Inverted) Incongruent Cued 1
## 5 1.5021 1 7 1.1124397 Familiar\n(Upright) Incongruent Uncued 1
## 6 1.3269 1 7 0.9826884 Unfamiliar\n(Inverted) Incongruent Cued 1Quality control
Summary stats per participant
ddply(df, c('subjId'), summarise, outlNumLow = median(outlNlo), outlNumHigh = median(outlNhi),
cuedCaught = sum(cued & corr), uncuedCaught = sum(!cued & corr))## subjId outlNumLow outlNumHigh cuedCaught uncuedCaught
## 1 1 1 7 112 115
## 2 14 0 7 116 116
## 3 17 0 9 115 116
## 4 19 0 10 113 107
## 5 21 0 6 118 116
## 6 23 0 6 117 116
## 7 24 0 0 105 101
## 8 25 0 9 115 116
## 9 26 0 9 118 113
## 10 27 0 11 110 115
## 11 28 1 7 115 116
## 12 29 0 14 114 112
## 13 30 0 5 118 117
## 14 31 0 7 116 115
## 15 32 0 7 119 114
## 16 33 0 11 107 104sts across trials
for(curSubjN in 1:nSubj){
curSubjId <- levels(df$subjId)[curSubjN]
ss <- df[df$subjId==curSubjId,]
p <- ggplot(data=ss, aes(x=trialN, y=st)) + geom_line() + theme_bw() + ylim(.5,2) +
labs(x='Trial Number', y='Suppression Time', title=paste('Participant', as.character(curSubjId))) +
theme(panel.grid.minor.x=element_blank(), panel.grid.minor.y=element_blank(),
axis.text=element_text(size=8), axis.title=element_text(size=9),
legend.text=element_text(size=8), legend.title=element_text(size=9),
legend.key = element_blank(), #legend.margin=unit(-.04, 'in'),
legend.background = element_rect(fill='transparent'))
plot(p)
}
## Warning: Removed 1 rows containing missing values (geom_path).
## Warning: Removed 1 rows containing missing values (geom_path).
## Warning: Removed 1 rows containing missing values (geom_path).
Individual plots
Congruence X Familiarity X Cue
for(curSubjN in 1:nSubj){
curSubjId <- levels(df$subjId)[curSubjN]
ss <- df[df$subjId==curSubjId,]
p <- ggplot(data=ss, aes(x=factor(Familiarity), y=st, colour=factor(Congruency))) + geom_boxplot() +
facet_grid(~Attention) + theme_bw() +
labs(x='Familiarity', y='Suppression Time', colour='Congruency',
title=paste('Participant', as.character(curSubjId))) +
theme(panel.grid.minor.x=element_blank(), panel.grid.minor.y=element_blank(),
axis.text=element_text(size=8), axis.title=element_text(size=9),
legend.text=element_text(size=8), legend.title=element_text(size=9),
legend.key = element_blank(), #legend.margin=unit(-.04, 'in'),
legend.background = element_rect(fill='transparent'))
plot(p)
}
Group plots
Is cueing effective across participants?
p <- ggplot(data=df, aes(x=Attention, y=st)) + geom_boxplot() + facet_grid(~subjId) + theme_bw() +
labs(x='Attention effect X Subject', y='Suppression Time', colour='Attention',
title='Effect of attentional cueing') +
theme(panel.grid.minor.x=element_blank(), panel.grid.minor.y=element_blank(),
axis.text=element_text(size=8), axis.title=element_text(size=9),
legend.text=element_text(size=8), legend.title=element_text(size=9),
legend.key = element_blank(), #legend.margin=unit(.0, 'in'),
legend.background = element_rect(fill='transparent'))
plot(p)
Is cueing effective across trials?
p <- ggplot(data=df, aes(x=Attention, y=st)) + geom_boxplot() + facet_grid(~bin) + theme_bw() +
labs(x='Attention effect X 100-trial bins', y='Suppression Time', colour='Attention',
title='Effect of attentional cueing') +
theme(panel.grid.minor.x=element_blank(), panel.grid.minor.y=element_blank(),
axis.text=element_text(size=8), axis.title=element_text(size=9),
legend.text=element_text(size=8), legend.title=element_text(size=9),
legend.key = element_blank(), #legend.margin=unit(.0, 'in'),
legend.background = element_rect(fill='transparent'))
plot(p)
Congruence X Familiarity X Cue
dfSum <- ddply(df, .(subjId, Attention, Familiarity, Congruency), summarise,
`Mean Normalized ST` = mean(stNorm, na.rm=T))
p <- ggplot(data=dfSum, aes(x=factor(Familiarity), y=`Mean Normalized ST`,
colour=factor(Congruency))) +
geom_boxplot() + facet_grid(~Attention) + theme_bw() +
labs(x='Familiarity', y='Suppression Time', colour='Congruency',
title='Congruence X Familiarty X Cue') +
theme(panel.grid.minor.x=element_blank(), panel.grid.minor.y=element_blank(),
axis.text=element_text(size=8), axis.title=element_text(size=9),
legend.text=element_text(size=8), legend.title=element_text(size=9),
legend.key = element_blank(), #legend.margin=unit(-.04, 'in'),
legend.background = element_rect(fill='transparent'))
plot(p)
p <- ggplot(data=dfSum, aes(x=factor(Familiarity), y=`Mean Normalized ST`,
group=factor(subjId), colour=factor(subjId))) +
geom_line() + facet_grid(~Attention*Congruency) + theme_bw() +
labs(x='Familiarity', y='Suppression Time', colour='Congruency',
title='Congruence X Familiarty X Cue') +
theme(panel.grid.minor.x=element_blank(), panel.grid.minor.y=element_blank(),
axis.text=element_text(size=8), axis.title=element_text(size=9),
legend.text=element_text(size=8), legend.title=element_text(size=9),
legend.key = element_blank(), #legend.margin=unit(-.04, 'in'),
legend.background = element_rect(fill='transparent'))
plot(p)
Linear models
# Centered data set: